In [102]:
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Project Python Foundations: FoodHub Data Analysis¶
Marks: 60
Context¶
The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.
The app allows the restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin of the delivery order from the restaurants.
Objective¶
The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company to improve the business.
Data Description¶
The data contains the different data related to a food order. The detailed data dictionary is given below.
Data Dictionary¶
- order_id: Unique ID of the order
- customer_id: ID of the customer who ordered the food
- restaurant_name: Name of the restaurant
- cuisine_type: Cuisine ordered by the customer
- cost: Cost of the order
- day_of_the_week: Indicates whether the order is placed on a weekday or weekend (The weekday is from Monday to Friday and the weekend is Saturday and Sunday)
- rating: Rating given by the customer out of 5
- food_preparation_time: Time (in minutes) taken by the restaurant to prepare the food. This is calculated by taking the difference between the timestamps of the restaurant's order confirmation and the delivery person's pick-up confirmation.
- delivery_time: Time (in minutes) taken by the delivery person to deliver the food package. This is calculated by taking the difference between the timestamps of the delivery person's pick-up confirmation and drop-off information
Let us start by importing the required libraries¶
In [103]:
import plotly.io as pio
pio.renderers.default='notebook'
# import libraries for data manipulation
import numpy as np
import pandas as pd
# import libraries for data visualization
import matplotlib.pyplot as plt
import seaborn as sns
!pip install plotly
import plotly.express as px
import plotly.figure_factory as ff
# to restrict the float value to 2 decimal places
pd.set_option('display.float_format', lambda x: '%.2f' % x)
# To display the graphs
%matplotlib inline
Understanding the structure of the data¶
In [104]:
# giving colab access to drive
from google.colab import drive
drive.mount('/content/drive')
In [105]:
# importing the data
df = pd.read_csv('/content/drive/MyDrive/DS&BA @ UTA/Module 1/Project - FoodHub/foodhub_order.csv')
# returning the first 5 rows to check all data was correclty imported
df.head()
Out[105]:
Observations:¶
- The DataFrame has 9 columns as mentioned in the Data Dictionary.
- Data in each row corresponds to the order placed by a customer.
- Data seems correctly imported.
Question 1: How many rows and columns are present in the data? [0.5 mark]¶
In [106]:
# looking at the total columns and rows of the df
df.shape
Out[106]:
Observations:¶
- There are 1,898 rows and 9 columns in the data set.
Question 2: What are the datatypes of the different columns in the dataset? (The info() function can be used) [0.5 mark]¶
In [107]:
# printing the information summary of the DataFrame
df.info()
Observations:¶
- There are no missing values in any of the columns.
- There are 4 object columns including 'rating', and 5 numeric columns
Question 3: Are there any missing values in the data? If yes, treat them using an appropriate method. [1 mark]¶
In [108]:
# looking for missing values
df.isnull().sum()
Out[108]:
Observations:¶
- There are no missing values.
Question 4: Check the statistical summary of the data. What is the minimum, average, and maximum time it takes for food to be prepared once an order is placed? [2 marks]¶
In [109]:
# looking at the descriptive overview of the data
df.describe(include='all').T
Out[109]:
Observations:¶
- The average time for food preparation is 27 minutes, with the minimum time being 20 minutes and the maximum being 35 minutes
- The average cost per order is 16.5 dollars, with the minimum being 4.47 and the maximum being 35.41
- The average delivery time is 24 minutes, with the minimum time being 15 minutes and the maximum being 33 minutes
Question 5: How many orders are not rated? [1 mark]¶
In [110]:
# looking at the count of each rating value
df['rating'].value_counts()
Out[110]:
Observations:¶
- Out of 1898 orders, 736 were not rated. This constitutes 38.7% of the total, which is the topmost recurring value.
Exploratory Data Analysis (EDA)¶
Univariate Analysis¶
Question 6: Explore all the variables and provide observations on their distributions. (Generally, histograms, boxplots, countplots, etc. are used for univariate exploration.) [9 marks]¶
In [111]:
fig=px.histogram(df, x='cost_of_the_order')
fig.show()
fig=px.box(df, x='cost_of_the_order')
fig.show()
- The median cost for the orders is 14, with 75% of the orders being less than 22.3
- The minimum cost is 4.47 and the maximum is 35.4.
- Orders with costs ranging between 12 and 13 dollars have the highest recurrences (340 total orders).
In [112]:
fig=px.histogram(df, x='food_preparation_time')
fig.show()
fig=px.box(df, x='food_preparation_time')
fig.show()
- Food preparation time ranges from 20 to 35 minutes.
- The data is equally distributed for the most part.
In [113]:
fig=px.histogram(df, x='delivery_time')
fig.show()
fig=px.box(df, x='delivery_time')
fig.show()
- 50% of the orders had a delivery time between 20 and 28 minutes.
- The minimum delivery time was 15 minutes and the maximum was 33.
In [114]:
fig=px.histogram(df, x='restaurant_name')
fig.show()
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# calculating the percentages of orders from each restaurant
df.restaurant_name.value_counts()*100 / len(df)
Out[115]:
- Shake Shack is the number one restaurant with 219 orders (11.5%).
- The Meatball Shop and Blue Ribbon Sushi follow second place with 132(6.9%) and 119(6.2%) orders respectively.
- Blue Ribbon Fried Chicken is in fourth place with 96 orders (5%).
In [116]:
fig=px.histogram(df, x='cuisine_type')
fig.show()
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# calculating the percentages of orders from each type of cuisine
df.cuisine_type.value_counts()*100 / len(df)
Out[117]:
- 30% of the orders come from American cuisine, followed by Japanese with 24.7% and Italian in third place with 15.7%.
- Chinese cuisine is the fourth most popular cuisine with 11.3%.
In [118]:
fig=px.histogram(df, x='day_of_the_week')
fig.show()
In [119]:
# calculating the percentages of orders depending on the days of the week
df.day_of_the_week.value_counts()*100 / len(df)
Out[119]:
- 71% of orders are made on weekends (Saturdays and Sundays), while only 29% are made during weekdays.
In [120]:
fig=px.histogram(df, x='rating')
fig.show()
In [121]:
# calculating the percentages for each type of rating given
df['rating'].value_counts()*100 / len(df)
Out[121]:
- As stated before, most orders weren't ranked (38.7%).
- 30.9% of the orders were ranked with 5 stars, 20.2% were ranked 4 stars, and the remaining 9.9% were ranked with 3 stars.
Question 7: Which are the top 5 restaurants in terms of the number of orders received? [1 mark]¶
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# calculating the total number of orders from each restaurant
rest_name = ['restaurant_name']
for column in rest_name:
print(df[column].value_counts(ascending=False))
Observations:¶
- As seen before, Shake Shack is the number one restaurant with a total of 219 orders, followed by The Meatball Shop with 132, Blue Ribbon Sushi with 119, Blue Ribbon Fried Chicken with 96, and finally in fifth place Parm with 68 orders.
Question 8: Which is the most popular cuisine on weekends? [1 mark]¶
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# calculating the total number of orders from each type of cuisine
df['cuisine_type'].value_counts(ascending=False)
Out[123]:
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# calculating the total number of orders during weekends from each type of cuisine
df.loc[df['day_of_the_week']=='Weekend','cuisine_type'].value_counts(ascending=False)
Out[124]:
Observations:¶
- American cuisine is the most popular overall, with 71% of their orders being made on weekends.
Question 9: What percentage of the orders cost more than 20 dollars? [2 marks]¶
In [125]:
# getting the sum of all orders with a cost over 20
df.loc[df['cost_of_the_order']>20].shape
Out[125]:
In [126]:
# getting total of orders
df.shape
Out[126]:
In [127]:
# getting the % of orders over 20
orders_over_twenty=(555/1898)*100
print(orders_over_twenty)
Observations:¶
- Only 29% of orders have a total cost of over 20 dollars.
Question 10: What is the mean order delivery time? [1 mark]¶
In [128]:
# getting the mean of the delivery time
df['delivery_time'].mean()
Out[128]:
Observations:¶
- The average delivery time is 24 minutes.
Question 11: The company has decided to give 20% discount vouchers to the top 3 most frequent customers. Find the IDs of these customers and the number of orders they placed. [1 mark]¶
In [129]:
# finding the top most recurring users
df['customer_id'].value_counts(ascending=False)
Out[129]:
Observations:¶
- Customer ID 52832 is the most frequent user with a total of 13 orders, followed by Customer ID 47440 with 10 orders and Customer ID 83287 with 9.
Multivariate Analysis¶
Question 12: Perform a multivariate analysis to explore relationships between the important variables in the dataset. (It is a good idea to explore relations between numerical variables as well as relations between numerical and categorical variables) [10 marks]¶
In [130]:
# corr analysis between numerical variables
sns.pairplot(df)
plt.show()
- There's no correlation between any of the numerical variables.
In [131]:
fig=px.box(df,x='cuisine_type',y='cost_of_the_order')
fig.show()
In [132]:
fig=px.scatter(df,x='cuisine_type',y='cost_of_the_order', color='day_of_the_week')
fig.show()
- French cuisine has the highest median price (20.47 dollars) and Vietnamese the lowest (12 dollars).
- The most expensive order comes from a Mediterranean restaurant (35.4 dollars) and the cheapest one comes from a Japanese restaurant (4.47 dollars).
- Spanish and Korean cuisine only have one order each made on a weekend, with most of their orders being placed during weekdays.
- Costs of the orders between the four most popular cuisines (American, Japanese, Italian and Chinese) tend to be very similar to each other.
- Day of the week doesn't seem to have an impact on the cost of the order.
In [133]:
fig=px.box(df,x='cuisine_type',y='food_preparation_time')
fig.show()
- Most couisines have a similar range of food preparation time.
In [134]:
fig=px.scatter(df,x='cuisine_type',y='food_preparation_time', color='day_of_the_week')
fig.show()
- Day of the week doesn't seem to have an impact on food preparation time.
In [135]:
fig=px.box(df,x='cuisine_type',y='delivery_time')
fig.show()
- Most cuisines have a similar delivery time, but French and Vietnamese cuisines tend to be a little higher. This is most likely due to few order counts (little data to look at) and could be related to the distance between the restaurants and their destinations.
In [136]:
fig=px.scatter(df,x='cuisine_type',y='delivery_time', color='day_of_the_week')
fig.show()
In [137]:
fig=px.box(df,x='day_of_the_week',y='delivery_time')
fig.show()
- We can see that day of the week does have a significant impact on the delivery time.
- During weekdays the delivery time tends to be higher, ranging from 24 to 33 minutes.
- Delivery time during weekends can be as low as 15 minutes, with 50% of the orders being under 22, while 75% of orders placed on weekdays have a delivery time over 26.
In [138]:
fig=px.histogram(df,x='cuisine_type',color='day_of_the_week')
fig.show()
- Most orders are made during weekends, averaging around 59.5% of the total orders for each cuisine.
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fig=px.histogram(df,x='cuisine_type',color='rating')
fig.show()
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# looking at the count of 5 stars ratings given per cuisine
df.loc[df['rating']=='5','cuisine_type'].value_counts(ascending=False)
Out[140]:
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# looking at the count of 4 stars ratings given per cuisine
df.loc[df['rating']=='4','cuisine_type'].value_counts(ascending=False)
Out[141]:
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# looking at the count of 3 stars ratings given per cuisine
df.loc[df['rating']=='3','cuisine_type'].value_counts(ascending=False)
Out[142]:
In [143]:
# looking at the count of not rated orders per cuisine
df.loc[df['rating']=='Not given','cuisine_type'].value_counts(ascending=False)
Out[143]:
- Out of the rated orders, 5 stars was the most frequently given value to most cuisines.
In [144]:
fig=px.histogram(df,x='restaurant_name',y='cost_of_the_order')
fig.show()
In [145]:
# looking for the average cost per order from Shake Shack
df.loc[df['restaurant_name']=='Shake Shack','cost_of_the_order'].mean()
Out[145]:
- Shake Shack made a total revenue of 3,579.5 dollars from FoodHub orders, with an average cost of 16.3 dollars per order.
In [146]:
fig=px.box(df,x='day_of_the_week',y='cost_of_the_order')
fig.show()
- Day of the week doesn't impact how much people spend on food delivery.
In [147]:
fig=px.box(df,x='rating',y='cost_of_the_order')
fig.show()
In [148]:
# looking at the average cost of orders depending on the rating given
df.groupby(['rating'])[['cost_of_the_order']].mean()
Out[148]:
- Cost of the order doesn't have a meaningful impact on the rating given.
In [149]:
fig=px.histogram(df,x='day_of_the_week',color='rating')
fig.show()
- Ratings given to orders placed on weekends are closely proportional to those given on weekdays.
In [150]:
fig=px.box(df,x='rating',y='food_preparation_time')
fig.show()
In [151]:
# looking at the average food preparation time categorized by rating
df.groupby(['rating'])[['food_preparation_time']].mean()
Out[151]:
In [152]:
fig=px.box(df,x='rating',y='delivery_time')
fig.show()
In [153]:
# looking at the average delivery time categorized by rating
df.groupby(['rating'])[['delivery_time']].mean()
Out[153]:
In [154]:
# creating new column with the total time of an order, from the time it's placed to when it gets to it's destination
df['total_time'] = df['delivery_time'] + df['food_preparation_time']
df['total_time'] = df['total_time'].astype(int)
In [155]:
# making sure the column was added correctly
df.head()
Out[155]:
In [156]:
fig=px.box(df,x='rating',y='total_time')
fig.show()
In [157]:
# looking at the average total time categorized by rating
df.groupby(['rating'])[['total_time']].mean()
Out[157]:
- Overall, orders rated 3 starts tend to take a bit longer, though this is not a significant time change compared to the rest.
Question 13: The company wants to provide a promotional offer in the advertisement of the restaurants. The condition to get the offer is that the restaurants must have a rating count of more than 50 and the average rating should be greater than 4. Find the restaurants fulfilling the criteria to get the promotional offer. [3 marks]¶
In [158]:
# looking at the types of 'rating' and their count
df['rating'].apply(type).value_counts()
Out[158]:
In [159]:
# replacing ''Not given' values with nan
df['rating'] = df['rating'].replace(['Not given'],np.nan)
# changing the data type to float
df['rating'] = df['rating'].astype(float)
In [160]:
# returning the different types of cuisines
df['cuisine_type'].unique()
Out[160]:
In [161]:
# looking at American restaurants and their rating count
df.loc[df['cuisine_type']=='American'].groupby("restaurant_name")["rating"].count()
Out[161]:
In [162]:
# looking at Blue Ribbon Fried Chicken and its average rating
df.loc[df['restaurant_name']=='Blue Ribbon Fried Chicken'].groupby("restaurant_name")["rating"].mean()
Out[162]:
In [163]:
# looking at Shake Shack and its average rating
df.loc[df['restaurant_name']=='Shake Shack'].groupby("restaurant_name")["rating"].mean()
Out[163]:
The next American restaurants qualify for the promotion:
- Blue Ribbon Fried Chicken
- Shake Shack
In [164]:
# looking at Korean restaurants and their rating count
df.loc[df['cuisine_type']=='Korean'].groupby("restaurant_name")["rating"].count()
Out[164]:
No Korean restaurants qualify for the promotion.
In [165]:
# looking at Japanese restaurants and their rating count
df.loc[df['cuisine_type']=='Japanese'].groupby("restaurant_name")["rating"].count()
Out[165]:
In [166]:
# looking at Blue Ribbon Sushi and its average rating
df.loc[df['restaurant_name']=='Blue Ribbon Sushi'].groupby("restaurant_name")["rating"].mean()
Out[166]:
Blue Ribbon Sushi is the only Japanese restaurant that qualifies for the promotion.
In [167]:
# looking at Mexican restaurants and their rating count
df.loc[df['cuisine_type']=='Mexican'].groupby("restaurant_name")["rating"].count()
Out[167]:
No Mexican restaurants qualify for the promotion.
In [168]:
# looking at Indian restaurants and their rating count
df.loc[df['cuisine_type']=='Indian'].groupby("restaurant_name")["rating"].count()
Out[168]:
No Indian restaurants qualify for the promotion.
In [169]:
# looking at Italian restaurants and their rating count
df.loc[df['cuisine_type']=='Italian'].groupby("restaurant_name")["rating"].count()
Out[169]:
In [170]:
# looking at The Meatball Shop and its average rating
df.loc[df['restaurant_name']=='The Meatball Shop'].groupby("restaurant_name")["rating"].mean()
Out[170]:
The Meatball Shop is the only Italian restaurant that qualifies for the promotion.
In [171]:
# looking at Mediterranean restaurants and their rating count
df.loc[df['cuisine_type']=='Mediterranean'].groupby("restaurant_name")["rating"].count()
Out[171]:
No Mediterranean restaurants qualify for the promotion.
In [172]:
# looking at Chinese restaurants and their rating count
df.loc[df['cuisine_type']=='Chinese'].groupby("restaurant_name")["rating"].count()
Out[172]:
No Chinese restaurants qualify for the promotion.
In [173]:
# looking at Middle Eastern restaurants and their rating count
df.loc[df['cuisine_type']=='Middle Eastern'].groupby("restaurant_name")["rating"].count()
Out[173]:
No Middle Eastern restaurants qualify for the promotion.
In [174]:
# looking at Thai restaurants and their rating count
df.loc[df['cuisine_type']=='Thai'].groupby("restaurant_name")["rating"].count()
Out[174]:
No Thai restaurants qualify for the promotion.
In [175]:
# looking at Southern restaurants and their rating count
df.loc[df['cuisine_type']=='Southern'].groupby("restaurant_name")["rating"].count()
Out[175]:
No Southern restaurants qualify for the promotion.
In [176]:
# looking at French restaurants and their rating count
df.loc[df['cuisine_type']=='French'].groupby("restaurant_name")["rating"].count()
Out[176]:
No French restaurants qualify for the promotion.
In [177]:
# looking at Spanish restaurants and their rating count
df.loc[df['cuisine_type']=='Spanish'].groupby("restaurant_name")["rating"].count()
Out[177]:
No Spanish restaurants qualify for the promotion.
In [178]:
# looking at Vietnamese restaurants and their rating count
df.loc[df['cuisine_type']=='Vietnamese'].groupby("restaurant_name")["rating"].count()
Out[178]:
No Vietnamese restaurants qualify for the promotion.
Observations:¶
- Only 4 restaurants qualify for the promotion: Shake Shack, The Meatball Shop, Blue Ribbon Fried Chicken, and Blue Ribbon Sushi.
Question 14: The company charges the restaurant 25% on the orders having cost greater than 20 dollars and 15% on the orders having cost greater than 5 dollars. Find the net revenue generated by the company across all orders. [3 marks]¶
In [179]:
# Looking at orders over 20 dollars from each week day category
df.loc[df['cost_of_the_order']>20].groupby("day_of_the_week")["cost_of_the_order"].count()
Out[179]:
In [180]:
# adding up the orders from above to get the total number of orders over 20
total_orders_above_twenty=158+397
print(total_orders_above_twenty)
In [181]:
# Looking at the sum of orders over 20 from each week day category
df.loc[df['cost_of_the_order']>20].groupby("day_of_the_week")["cost_of_the_order"].sum()
Out[181]:
In [182]:
# adding up the amounts from above to get the total cost of orders over 20
total_cost_orders_over_twenty=4174.49+10580.42
print(total_cost_orders_over_twenty)
In [183]:
# calculating the total revenue made from orders over 20
revenue_made_orders_over_twenty=14754.91*.25
print(revenue_made_orders_over_twenty)
In [184]:
# creating function to calculate revenue generated per order
def revenue_generated(*costs):
"""
This function takes the cost of the orders,
applies the corresponding interest,
and returns the total revenue generated by interests.
"""
total_revenue= 0
for cost in costs:
if cost > 20:
total_revenue += cost * .25
elif cost > 5:
total_revenue += cost * .15
else:
total_revenue += cost * 0
# return the total revenue
return total_revenue
In [185]:
# applying the function to the df
df['cost_of_the_order'].apply(revenue_generated)
Out[185]:
In [186]:
# adding a new column for the revenue
df['revenue']=df['cost_of_the_order'].apply(revenue_generated)
df.head()
Out[186]:
In [187]:
# adding up the revenue column
df['revenue'].sum()
Out[187]:
Observations:¶
- There's a total of 555 orders that cost more than 20 dollars each, adding up to a total cost of 14,754.91 dollars.
- FoodHub revenues a total of 3,688.72 dollars from the total orders over 20.
- The total revenue generated from all orders is 6,166.303 dollars.
Question 15: The company wants to analyze the total time required to deliver the food. What percentage of orders take more than 60 minutes to get delivered from the time the order is placed? (The food has to be prepared and then delivered.) [2 marks]¶
In [188]:
# getting the percentage of orders that took longer than 60 minutes
df.loc[df['total_time']>60].count()*100 / len(df)
Out[188]:
Observations:¶
- 10.54% of the orders took more than 60 minutes to cook and deliver in total.
Question 16: The company wants to analyze the delivery time of the orders on weekdays and weekends. How does the mean delivery time vary during weekdays and weekends? [2 marks]¶
In [189]:
# looking at the total of orders made on weekends
df.loc[df['day_of_the_week']=='Weekend'].groupby("day_of_the_week").count()
Out[189]:
There's a total of 1351 orders on weekends.
In [190]:
# returning the sum of all delivery time for orders during weekends
df.loc[df['day_of_the_week']=='Weekend'].groupby("day_of_the_week")['delivery_time'].sum()
Out[190]:
In [191]:
# rerturning the average delivery time for orders during weekends
df.loc[df['day_of_the_week']=='Weekend'].groupby("day_of_the_week")['delivery_time'].mean()
Out[191]:
In [192]:
# rerturning the average delivery time for orders during weekdays
df.loc[df['day_of_the_week']=='Weekday'].groupby("day_of_the_week")['delivery_time'].mean()
Out[192]:
Observations:¶
- Orders made during weekdays take more time to deliver, averaging 28.34 minutes.
- Orders placed on weekends tend to take 5.85 minutes less than those placed on weekdays.
Conclusion and Recommendations¶
Question 17: What are your conclusions from the analysis? What recommendations would you like to share to help improve the business? (You can use cuisine type and feedback ratings to drive your business recommendations.) [6 marks]¶
Conclusions:¶
- 38.7% of orders aren't rated.
- 75% of orders cost less than 22.3 dollars.
- The average cost per order is 16.5 dollars.
- The average delivery time is 24 minutes.
- The average food preparation time is 27 minutes.
- Shake Shack is the number one restaurant with 219 orders, which constitutes 11.5% of the total orders.
- The Meatball Shop and Blue Ribbon Sushi follow second place with 132(6.9%) and 119(6.2%) orders respectively.
- Blue Ribbon Fried Chicken is in fourth place with 96 orders (5%).
- 30% of the orders come from American cuisine, followed by Japanese with 24.7% and Italian in third place with 15.7%.
- 71% of orders are made on weekends (Saturdays and Sundays), while only 29% are made during weekdays.
- Only 29% of orders have a total cost of over 20 dollars.
- Customer ID 52832 is the most frequent user with a total of 13 orders, followed by Customer ID 47440 with 10 orders and Customer ID 83287 with 9.
- Costs of the orders between the four most popular cuisines (American, Japanese, Italian and Chinese) tend to be very similar to each other, with their median cost being roughly 14 dollars.
- Most cuisines have a similar delivery time, but French and Vietnamese cuisines tend to be a little higher. This is most likely due to few order counts (little data to look at) and could be related to the distance between the restaurants and their destinations.
- During weekdays the delivery time tends to be higher, ranging from 24 to 33 minutes.
- Delivery time during weekends can be as low as 15 minutes, while 75% of orders placed on weekdays have a delivery time over 26.
- Orders placed on weekends tend to take 5.85 minutes less than those placed on weekdays.
- Only 4 restaurants have more than 50 ratings, and an average rating being over 4 stars. These restaurants qualify for the advertisement promotional offer: Shake Shack, The Meatball Shop, Blue Ribbon Fried Chicken, and Blue Ribbon Sushi.
- The total revenue generated from all orders is 6,166.303 dollars.
- 10.54% of the orders took more than 60 minutes to cook and deliver in total.
Recommendations:¶
- Promotions and advertisements on popular restaurants could help boost order placements during weekdays, which currently stand at 29% of the total orders.
- Creating a loyalty program could encourage customers to place orders more frequently and become brand lovers, benefiting the overall revenue and popularity of the app.
- Discounts on orders over 25 could help boost the average cost per order.
- Discount coupons when consecutively rating a certain number of orders could help encourage customers to rate their orders and deliveries more often. This could be highly beneficial for a better understanding of how satisfied customers are with the deliveries and the restaurant’s guidelines for packaging and food safety. Action can then be implemented to solve related issues and achieve better customer satisfaction.
- Agency should focus on providing excellent customer service, and make sure the platform runs smoothly specifically during weekends, due to high demand.